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Paper   IPM / Cognitive Sciences / 15922
School of Cognitive Sciences
  Title:   Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron Model
1.  S. Rashid Shomali
2.  M. Nili Ahmadabadi
3.  S. N. Rasuli
4.  H. Shimazaki
  Status:   Published
  Journal: bioRxiv
  Year:  2019
  Pages:   1-28
  Supported by:  IPM
An appealing challenge in Neuroscience is to identify network architecture from neural activity. A key requirement is the knowledge of statistical input-output relation of single neurons in vivo. Using a recent exact solution of spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near threshold, we construct a unified framework that links synaptic inputs, spiking nonlinearity, and network architecture, with statistics of population activity. The framework predicts structured higher-order interactions of neurons receiving common inputs under different architectures: It unveils two network motifs behind sparse activity reported in visual neurons. Comparing model�??s prediction with monkey�??s V1 neurons, we found excitatory inputs to pairs explain the sparse activity characterized by negative triple-wise interactions, ruling out shared inhibition. While the model predicts variation in the structured activity according to local circuitries, we show strong negative interactions are in general a signature of excitatory inputs to neuron pairs, whenever background activity is sparse.

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